Author, as appears in the article.: Castane, Helena; Baiges-Gaya, Gerard; Hernandez-Aguilera, Anna; Rodriguez-Tomas, Elisabet; Fernandez-Arroyo, Salvador; Herrero, Pol; Delpino-Rius, Antoni; Canela, Nuria; Menendez, Javier A.; Camps, Jordi; Joven, Jorge;

Department: Medicina i Cirurgia

URV's Author/s: Camps Andreu, Jorge / Castañé Vilafranca, Helena / FERNANDEZ ARROYO, SALVADOR / HERRERO GIL, POL / Joven Maried, Jorge / Rodriguez Tomas, Elisabet

Keywords: Ultra performance liquid chromatography Support vector machine Sphingomyelin Review Receiver operating characteristic Proteomics Procedures Prevalence Pathogenesis Obesity Nuclear magnetic resonance imaging Nonalcoholic fatty liver Non-alcoholic fatty liver disease Nash Multiomics Metabolomics Metabolism Metabolic disorder Mass spectrometry Machine learning Liver transplantation Liver cell carcinoma Liver cell Liquid-chromatography Lipotoxicity Lipidomics Lipidome Lipid metabolism Lipid composition Learning algorithm Insulin dependent diabetes mellitus Insulin Hydrophilic interaction chromatography Huntington chorea Humans Human High performance liquid chromatography Gray matter Glycerophospholipid Glucose Gas chromatography Fatty liver Electrospray Dyslipidemia Disease exacerbation Diagnostic accuracy Diabetes mellitus Deep learning Data analysis Chloroform Biological marker Bariatric surgery Artificial intelligence Animals Animal Amino acid metabolism Alzheimer disease Adipose tissue Acylcarnitine

Abstract: Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until liver damage is irreversible. Given this complex situation, the search for noninvasive alternatives is clinically important. A hallmark of NAFLD progression is the dysregulation in lipid metabolism. In this context, recent advances in the area of machine learning have increased the interest in evaluating whether multi-omics data analysis performed on peripheral blood can enhance human interpretation. In the present review, we show how the use of machine learning can identify sets of lipids as predictive biomarkers of NAFLD progression. This approach could potentially help clinicians to improve the diagnosis accuracy and predict the future risk of the disease. While NAFLD has no effective treatment yet, the key to slowing the progression of the disease may lie in predictive robust biomarkers. Hence, to detect this disease as soon as possible, the use of computational science can help us to make a more accurate and reliable diagnosis. We aimed to provide a general overview for all readers interested in implementing these methods.

Thematic Areas: Química Molecular biology Materiais General medicine Farmacia Ensino Biochemistry & molecular biology Biochemistry

licence for use: https://creativecommons.org/licenses/by/3.0/es/

Author's mail: jorge.camps@urv.cat jorge.joven@urv.cat elisabet.rodriguezt@estudiants.urv.cat elisabet.rodriguezt@estudiants.urv.cat helena.castane@estudiants.urv.cat

Author identifier: 0000-0002-3165-3640 0000-0003-2749-4541

Record's date: 2023-08-05

Papper version: info:eu-repo/semantics/publishedVersion

Link to the original source: https://www.mdpi.com/2218-273X/11/3/473

Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/

Papper original source: Biomolecules. 11 (3): 1-21

APA: Castane, Helena; Baiges-Gaya, Gerard; Hernandez-Aguilera, Anna; Rodriguez-Tomas, Elisabet; Fernandez-Arroyo, Salvador; Herrero, Pol; Delpino-Rius, Ant (2021). Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview. Biomolecules, 11(3), 1-21. DOI: 10.3390/biom11030473

Article's DOI: 10.3390/biom11030473

Entity: Universitat Rovira i Virgili

Journal publication year: 2021

Publication Type: Journal Publications